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The most reliable way to evaluate a coding agent is to run it on a representative sample of your own repository tasks, with the same tools, permissions, context, time limits and compute for every candidate. Define acceptance criteria before execution, grade with task-specific and regression tests, inspect the patch, and report runtime, resource use, human intervention and infrastructure failures separately. Public benchmarks can provide context, but they cannot predict how an agent will perform in your codebase and workflow.
What a useful coding-agent evaluation actually measures
A coding agent is more than a language model. The result depends on the model, its prompting and system instructions, the agent scaffold, tool selection, repository context, permissions, retry policy, sampling settings, runtime and available compute. A benchmark score therefore describes an evaluated agent system and its particular setup, not a model in isolation.
Your evaluation should answer practical questions:
- Can the agent implement the requested behavior in the repositories your team maintains?
- Does it preserve existing behavior and pass relevant regression tests?
- Is the resulting patch maintainable, secure and consistent with local conventions?
- How often does it require a person to intervene, restart or repair its work?
- What time, token or service consumption does a successful task require?
- Does it work reliably in the permissions, tools and review process your team would actually adopt?
Do not collapse all of those dimensions into an unexplained single score. A fast agent that produces brittle patches, or a highly capable agent that needs constant supervision, may be a poor fit despite a high task-completion rate.
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Build a representative private task set
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Start with tasks taken from your own issue tracker, pull requests and maintenance queue. Preserve the original request, repository context and acceptance criteria instead of rewriting every task into a perfectly specified benchmark prompt. A realistic set should include the ambiguity and incomplete information engineers routinely face.
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Balance the set across work types:
- Bug fixes: Reproduce a defect, identify its cause and change behavior without breaking nearby functionality.
- Small features: Add a bounded capability with explicit user-visible or API behavior.
- Refactors: Change structure or implementation while preserving stated behavior and compatibility.
- Test work: Add missing coverage, repair unreliable tests or create a regression test for a known defect.
- Maintenance: Update configuration, dependencies, migrations, build scripts or operational code where those tasks are part of your team’s workload.
Include the languages, frameworks, build systems and repository layouts that dominate your work. Include both well-documented tasks and tasks that require the agent to discover conventions from code. If production security, data handling or performance constraints matter, include examples that expose those requirements to the grader or reviewer.
Keep the original difficulty profile
Do not select only tasks with a clean expected output or an easy automated test. Include tasks with different levels of ambiguity, cross-file changes and domain context. Record why each task was selected and classify its expected difficulty before any agent runs. That lets you see whether a candidate succeeds only on narrowly specified work.
There is no source-supported universal task count that guarantees a valid evaluation. The set should be large and varied enough to represent your workflow, while remaining feasible for repeated runs and human review. If you can repeat tasks, reserve some for a holdout set so prompt or rubric changes do not silently overfit the results.
Separate task selection from grading design
For every task, write the acceptance criteria before exposing it to a candidate. Criteria should describe observable outcomes, affected interfaces and constraints. State what must remain unchanged when that matters. Avoid criteria that merely restate an implementation choice unless the repository requires that choice.
Examples of observable criteria include:
- An endpoint returns the documented status and response shape for valid and invalid inputs.
- A parser accepts the specified syntax while rejecting malformed input without a crash.
- An existing command continues to produce its prior output for a regression fixture.
- A migration can be applied and rolled back under the supported database version.
Freeze the unit of comparison
Decide whether you are comparing complete products as your team would use them or isolating model behavior. For a procurement or workflow decision, the complete product is usually the meaningful unit. Use the same task prompt, repository snapshot, environment, tools, permissions and time budget for every candidate. If you intentionally vary any of these, label the result as a comparison of whole configurations rather than models.
Record the configuration
Save a configuration record with at least:
- Agent and model names and exact versions or release identifiers.
- System instructions, task prompt and any reusable repository guidance.
- Available tools, command allowlists, network access and file permissions.
- Repository commit, dependency lockfile and operating-system or container image.
- Context supplied to the agent, including issue text, documentation and prior discussion.
- Retry, timeout, sampling and parallelism settings.
- CPU, memory, storage, accelerator and service limits.
Keep this record with the patch and test output. A later score without its configuration is difficult to reproduce and should not be treated as a stable product property.
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Choose a fair execution policy
Give each candidate the same opportunity to inspect the repository, run tests and revise its patch. Decide in advance whether the agent may ask questions, whether a human may answer them, and whether an interrupted run can resume. If human answers are allowed, use a scripted response policy or record the exact interaction so the assistance is comparable.
Set a time or interaction budget that reflects the workflow you are evaluating. Report the budget and actual elapsed time; do not quietly extend a failing run for one candidate. If the team normally uses retries, give every candidate the same retry policy and count retries as part of the workflow cost.
Define grading before execution
Use deterministic tests where behavior is testable
Run tests written for the task and relevant existing regression tests. A patch should pass the requested behavior and avoid unacceptable breakage; compiling, looking plausible or changing the expected file is not sufficient. Anthropic’s evaluation guidance describes why deterministic grading fits coding agents: “Deterministic graders are natural for coding agents because software is generally straightforward to evaluate: does the code run and do the tests pass?” Anthropic’s January 9, 2026 guidance explains the approach.
Make the test command and environment explicit. Capture exit status, failed tests, logs and relevant warnings. Include tests that exercise edge cases identified in the acceptance criteria, not only the agent’s newly added tests. Review newly added tests for whether they genuinely detect the requested behavior rather than merely matching the implementation.
Add a documented human rubric for what tests miss
Automated checks cannot fully assess maintainability, security, architecture or compatibility. Use a rubric with named criteria and a consistent scale, and have reviewers record evidence from the patch. Suitable criteria include:
- Correctness beyond the visible test cases.
- Clarity and consistency with repository conventions.
- Appropriate error handling and input validation.
- Security and privacy implications of the change.
- Scope discipline: no unrelated rewrites or accidental generated-file changes.
- Test quality, including meaningful coverage and stable assertions.
- Operational impact, such as logging, observability, migrations or rollback behavior.
Define what constitutes an automatic failure, such as a security regression, data loss, broken public interface or inability to build the repository. Keep reviewer identity, time spent and disagreements in the record. A rubric does not make subjective judgments objective, but it makes them visible and comparable.
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Run the evaluation and preserve the full trajectory
Use a repeatable execution sequence
- Reset the repository to the recorded commit and provision the same environment for the candidate.
- Apply the prewritten task prompt and provide only the context permitted by the evaluation policy.
- Allow the agent to inspect files, run approved commands and edit within the stated permissions.
- Stop at the defined time, interaction or resource limit, even if the patch is incomplete.
- Run the prescribed task tests and regression suite without changing the patch to help it pass.
- Save the final diff, test output, tool trace, timestamps and resource measurements.
- Send the patch through the same human review rubric used for every candidate.
- Reset the environment before the next candidate or repetition.
Distinguish capability failures from environment failures
Log whether a run failed because the agent produced an incorrect patch, because a dependency or service was unavailable, or because the evaluation infrastructure malfunctioned. Anthropic’s analysis of coding-evaluation infrastructure shows that container, dependency and resource errors can create failures unrelated to model capability; its findings are described in “Quantifying infrastructure noise in agentic coding evals”.
Define rerun rules before collecting results. For example, a confirmed container outage might receive a clean rerun, while a command timeout caused by the candidate’s own process remains a candidate failure. Whatever rule you choose, apply it to every candidate and report the number and cause of reruns. Never silently remove infrastructure failures from the denominator.
Capture enough evidence to audit a result
For each task and run, retain:
- The exact prompt and all agent messages or tool calls.
- Repository commit, environment image and configuration record.
- Final patch, changed-file list and generated artifacts.
- Task-test and regression-test commands, output and exit codes.
- Start and end timestamps, retries and human interventions.
- Token, service, compute or other usage figures when the same measurement is available for all candidates.
- Infrastructure incidents and the disposition of any rerun.
- Reviewer findings, severity and time spent.
This evidence lets you investigate an unexpected score instead of treating it as a mysterious model property.
Compare results on separate axes
Report a profile rather than one opaque ranking. At minimum, include task completion and regression safety. Add the following when they can be measured consistently:
- Task success: Whether all mandatory acceptance criteria passed.
- Regression safety: Whether existing and task-relevant behavior remained intact.
- Patch quality: Human rubric findings, including maintainability and security.
- Reliability: Consistency across repeated runs and rate of non-capability failures.
- Runtime: Wall-clock time from task start to final grading.
- Consumption: Tokens, service calls or compute cost measured with one definition for every candidate.
- Human intervention: Questions answered, manual edits, restarts and review time.
- Workflow fit: Whether permissions, tools, repository context and review integration match your operating model.
State the weighting if you create a composite decision score. There is no evidence-based universal weighting: a regulated service may prioritize security and intervention rate, while a maintenance team may prioritize throughput and compatibility. Preserve the underlying axis results so a different team can reach a different decision from the same evidence.
Use repetition to measure reliability
A single run can be unusually good or bad. Repeat tasks when the agent’s sampling, planning or tool behavior is nondeterministic, keeping the task and environment constant. Report the proportion of runs that meet the acceptance criteria and the spread of runtime or intervention, not only the best result. If repeated runs are too expensive for every task, use a smaller, predeclared reliability subset and keep it unchanged across candidates.
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Interpret variability alongside task difficulty. An agent that succeeds on average but occasionally introduces a severe regression may be less suitable than one with a slightly lower mean success rate and predictable failure modes. Your release or adoption policy should specify how severe failures are treated.
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SWE-bench Verified: useful context, not a deployment guarantee
SWE-bench Verified is a human-validated subset of 500 software-engineering instances. It provides a shared issue-resolution format drawn from real repositories, but its score still reflects the evaluated agent system, scaffold, tools and resources used for that run.
OpenAI’s February 23, 2026 reassessment, “Why we no longer evaluate SWE-bench Verified,” says design and contamination concerns mean improvements on this benchmark no longer provide a reliable signal of meaningful real-world software-development progress. That is a reason to avoid using a current leaderboard position as a proxy for performance on your stack, not a reason to ignore every benchmark result. Treat it as one contextual data point and verify capabilities with private tasks.
The benchmark’s history and purpose are described in OpenAI’s August 13, 2024 introduction. Neither page establishes that a particular commercial agent is best for your repositories.
SWE-Lancer broadens the represented task format
OpenAI’s Evals catalog describes SWE-Lancer as more than 1,400 freelance software-engineering tasks from Upwork, with a stated total task value of $1 million USD. Those figures describe the size and stated value of the task set, not current agent performance. The broader freelance-task format can complement repository issue benchmarks, but it still does not reproduce your requirements, codebase, permissions or review process.
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Phase 1: prepare
- Write the decision you need to make, such as selecting a tool for bug fixing, deciding whether to permit autonomous test changes or estimating review savings.
- Assemble a task set from representative repositories and classify each task by type, language, ambiguity and risk.
- Write acceptance criteria, mandatory tests, regression commands and automatic-failure conditions for every task.
- Choose the candidate configurations and freeze versions, prompts, tools, permissions, context and resource limits.
- Define measurement formulas, intervention rules, rerun policy and any weighting before execution.
Phase 2: execute
- Provision a clean, identical environment for each run.
- Start the timer, apply the task and allow only the declared interactions.
- Record the complete trace and stop at the agreed limit.
- Run task-specific tests followed by the relevant regression suite.
- Classify failures as capability, specification, human-policy or infrastructure failures using the predeclared rules.
- Repeat the reliability subset and reset the environment between runs.
Phase 3: review and decide
- Apply the same patch-quality and security rubric to every successful-looking patch.
- Compile per-task evidence before calculating aggregate rates.
- Report axis-level results, confidence or uncertainty where repetitions permit it, and all excluded or rerun cases.
- Examine failures by category: misunderstanding, incomplete implementation, test avoidance, regression, tool misuse, timeout or infrastructure error.
- Make the adoption decision against the team’s priorities and define safeguards, permissions and review requirements for a pilot.
Common evaluation mistakes and how to correct them
Ranking agents from incompatible runs
Different prompts, repository snapshots, tool permissions or timeouts make the comparison ambiguous. Freeze the complete setup, or explicitly present the results as a comparison of different products in their default configurations rather than a model contest.
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Counting compilation as success
Compilation proves only that one stage of the toolchain completed. Require the requested behavior, relevant regression tests and the human checks that automated tests cannot express.
Letting each candidate define its own tests
An agent can write tests that encode its mistaken interpretation. Predeclare the grader and review newly added tests for meaningful coverage. Run independent regression checks whenever possible.
Ignoring infrastructure noise
A missing dependency, exhausted memory limit or broken container can look like an agent failure. Capture resource configuration and classify incidents separately, while applying one rerun policy to all candidates.
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Using a public score as a promise
Benchmarks represent particular task formats and evaluation systems. SWE-bench Verified’s current design and contamination concerns are specifically documented by OpenAI, so a high score should not be presented as evidence that an agent will solve your private backlog.
Publishing a single average
An average can hide security failures, highly variable runs or extensive human repair. Publish the underlying task outcomes and separate success, safety, quality, cost, time and intervention.
What a decision-ready report contains
A useful report allows an engineer who did not run the study to reproduce its interpretation. Include:
- Scope, decision question, repositories and task-selection rationale.
- Candidate versions and the complete execution configuration.
- Acceptance criteria, test commands, review rubric and failure policy.
- Per-task outcomes with capability and infrastructure failures distinguished.
- Runtime, consumption, intervention and review effort under consistent definitions.
- Representative patches and anonymized traces for important successes and failures.
- Known limitations, including tasks or dimensions the set does not represent.
- A recommendation tied to explicit thresholds or priorities, plus safeguards for a pilot.
Keep the task set and grader versioned. When the repository, agent, model or workflow changes materially, rerun a comparable holdout rather than treating old results as current.
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Evaluate coding agents where you expect them to work: on your repositories, your requirements and your review process. Hold the full setup constant, define success before the run, test behavior and regressions, inspect the patch, and report reliability, resources, human effort and infrastructure failures alongside completion. Public benchmarks such as the 500-instance SWE-bench Verified set and the broader SWE-Lancer task catalog can help explain task formats, but the evidence needed for an adoption decision comes from a controlled evaluation of your own engineering workflow.
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